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Record W4406525222 · doi:10.21799/frbp.wp.2025.03

How Wealth and Age Interact to Affect Entrepreneurship

2025· report· en· W4406525222 on OpenAlexfundno aff
Philippe D'Astous, Vyacheslav Mikhed, Sahil Raina, Barry Scholnick

Bibliographic record

VenueWorking paper · 2025
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaAlberta Gambling Research Institute, University of Calgary
KeywordsAffect (linguistics)EntrepreneurshipPsychologyBusinessCommunicationFinance

Abstract

fetched live from OpenAlex

Using wealth windfalls from lottery winnings and matched employer-employee tax files, we compare the effect of additional wealth on the entrepreneurial activity of older and younger individuals.We find that additional wealth leads older winners (aged 55 and older) to reduce business ownership and scale.In contrast, additional wealth leads younger winners to increase business ownership and performance.We also show that extra lottery wealth reduces the wage labor supply of both younger and older individuals.Thus, while younger lottery winners reduce wage labor to increase entrepreneurship, older lottery winners reduce both wage labor and entrepreneurship to retire.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.283
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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